Speech Recognition Learning System Context Adaptation
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Solution Overview
Problem
Existing speech recognition systems fail to produce deterministic results due to environmental and speaker-specific factors, leading to variations in speech recognition accuracy across different contexts.
Innovation Solution
A speech recognition learning system that optimizes speech recognition by generating and applying contextual information-based implementation rules, iteratively refining stimulus data packages to improve recognition accuracy through a combination of stimulus data package generators, speech improvement processors, and knowledge databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single ASR engine is used for speech recognition, then the system structure is simple, but the recognition accuracy varies across different contexts and environments
Solution Approach 1:
The system segments speech recognition into multiple specialized ASR engines, each optimized for specific contexts or environments. Instead of using a single general-purpose engine, the system divides the recognition task across multiple engines that can be selectively applied based on the contextual requirements, thereby improving adaptability while managing complexity through modular architecture.
Solution Approach 2:
The system dynamically selects and switches between different ASR engines based on the detected context, environment, and speaker characteristics. This dynamic adaptation allows the system to optimize recognition accuracy for each specific situation rather than relying on a static single-engine approach, resolving the contradiction between versatility and complexity through intelligent resource allocation.
2Adaptability or versatility
If statistical principles are used for speech recognition, then the system can handle varied speech patterns, but the results are non-deterministic and vary across recognition events
Solution Approach 1:
The system incorporates feedback mechanisms that analyze the outcomes of speech recognition events and use this information to improve future recognition accuracy. By continuously learning from past performance across different contexts and speakers, the system reduces the non-deterministic nature of statistical recognition while maintaining its ability to handle varied speech patterns.
Solution Approach 2:
The system adjusts recognition parameters and model selections based on detected speech characteristics, environmental conditions, and contextual information. By dynamically changing parameters such as acoustic models, language models, and processing thresholds, the system achieves more consistent and deterministic results across different recognition events while preserving adaptability to varied speech patterns.
3Measurement precision
If speech recognition does not account for environmental context, then the system operation is simple, but the recognition accuracy deteriorates in different environments
Solution Approach 1:
The system performs preliminary analysis of environmental context, speaker characteristics, and speech patterns before executing the main recognition task. By pre-processing and categorizing contextual information in advance, the system can select the most appropriate ASR engine and parameters, thereby improving recognition accuracy without adding significant complexity to the core recognition process.
Data Source
AI summary
One or more embodiments include a speech recognition learning system for improved speech recognition. The learning system may include a speech optimizing system. The optimizing system may receive a first stimulus data package including spoken utterances having at least one phoneme, and contextual information. A number of result data packages may be retrieved which include stored spoken utterances and contextual information. A determination may be made as to whether the first stimulus data package requires improvement. A second stimulus data package may be generated based on the determination. A number of speech recognition implementation rules for implementing the second stimulus data package may be received. The rules may be associated with the contextual information. A determination may be made as to whether the second stimulus data package requires further improvement. Based on the determination, one or more additional speech recognition implementation rules for improved speech recognition may be generated.